Biotech Portfolio Risk and Returns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# fix_yahoo_finance is used to fetch data 
import fix_yahoo_finance as yf
yf.pdr_override()

# input
# Biotech Stock
symbols = ['AXSM','BBIO','KOD','ABBV','CELG','EXEL','LGND','VRTX','INCY','NVS','LLY','MGNX','CALA','AGEN','MRUS']
start = '2016-01-01'
end = '2019-01-01'

df = yf.download(symbols,start,end)['Adj Close']

[*********************100%***********************]  15 of 15 downloaded

#df = pd.DataFrame()
#for s in symbols:
#    df[s] = yf.download(s,start,end)['Adj Close']

from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
3 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

1092

df.head()

	ABBV	AGEN	AXSM	CALA	CELG	EXEL	INCY	KOD	LGND	LLY	MGNX	MRUS	NVS	VRTX
2016-01-04	48.749779	4.46	12.19	7.44	117.620003	5.49	103.320000	NaN	102.470001	74.540886	29.410000	NaN	66.679474	122.889999
2016-01-05	48.546680	4.43	14.98	7.79	117.959999	5.27	103.800003	NaN	100.650002	75.656258	28.520000	NaN	67.037170	123.449997
2016-01-06	48.555145	4.25	14.47	7.04	116.709999	5.29	102.260002	NaN	102.019997	75.179520	27.620001	NaN	66.119591	122.230003
2016-01-07	48.411289	3.89	12.45	6.41	111.889999	4.93	95.760002	NaN	98.620003	73.227615	26.070000	NaN	65.272011	114.959999
2016-01-08	47.091213	3.78	11.86	5.98	108.980003	4.89	94.779999	NaN	97.919998	73.083710	24.820000	NaN	63.421318	110.709999

df.tail()

	ABBV	AGEN	AXSM	CALA	CELG	EXEL	INCY	KOD	LGND	LLY	MGNX	MRUS	NVS	VRTX
2018-12-24	79.658409	2.22	2.15	3.72	59.209999	17.469999	58.500000	7.34	128.360001	104.393227	11.75	13.965	71.598831	151.910004
2018-12-26	84.277382	2.50	2.05	3.88	62.500000	18.950001	62.020000	8.22	137.860001	108.637405	12.50	13.400	73.577797	161.839996
2018-12-27	85.100853	2.39	2.00	3.78	62.810001	19.260000	62.340000	7.25	136.889999	110.397659	12.59	12.800	72.970222	162.369995
2018-12-28	86.246124	2.39	2.18	3.85	62.430000	19.440001	62.279999	6.62	135.839996	111.678741	12.61	13.730	74.003105	161.419998
2018-12-31	87.258888	2.38	2.82	4.01	64.089996	19.670000	63.590000	7.10	135.699997	113.165184	12.70	14.000	74.480492	165.710007

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title('Biotech Stocks Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x1e1bca11b38>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title('Biotech Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x1e1bfc81f28>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title('Biotech Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x1e1bfa924a8>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title('Biotech Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x1e1bfb0dda0>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(7,7))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Blues")

<matplotlib.axes._subplots.AxesSubplot at 0x1e1c7bbacc0>

# Box plot
stock_rets.plot(kind='box',figsize=(12,8))

<matplotlib.axes._subplots.AxesSubplot at 0x1e1c7d439b0>

rets = stock_rets.dropna()

plt.figure(figsize=(12,8))
plt.scatter(rets.mean(), rets.std(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Expected returns')
plt.ylabel('Risk')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(12,8))
plt.scatter(rets.mean(), rets.std(), s=area)
plt.xlabel("Expected Return", fontsize=15)
plt.ylabel("Risk", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.mean(), rets.std()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

VRTX  VRTX    1.000000
NVS   NVS     1.000000
AGEN  AGEN    1.000000
AXSM  AXSM    1.000000
CALA  CALA    1.000000
CELG  CELG    1.000000
EXEL  EXEL    1.000000
INCY  INCY    1.000000
KOD   KOD     1.000000
LGND  LGND    1.000000
LLY   LLY     1.000000
MGNX  MGNX    1.000000
MRUS  MRUS    1.000000
ABBV  ABBV    1.000000
CELG  VRTX    0.735707
VRTX  CELG    0.735707
      LGND    0.664595
LGND  VRTX    0.664595
ABBV  VRTX    0.654258
VRTX  ABBV    0.654258
LLY   ABBV    0.634248
ABBV  LLY     0.634248
CELG  NVS     0.607962
NVS   CELG    0.607962
ABBV  CELG    0.598175
CELG  ABBV    0.598175
VRTX  INCY    0.597989
INCY  VRTX    0.597989
CELG  INCY    0.592354
INCY  CELG    0.592354
                ...   
MRUS  INCY    0.102754
INCY  MRUS    0.102754
LLY   KOD     0.101754
KOD   LLY     0.101754
AGEN  AXSM    0.087025
AXSM  AGEN    0.087025
ABBV  AXSM    0.074573
AXSM  ABBV    0.074573
LLY   MRUS    0.073959
MRUS  LLY     0.073959
EXEL  MRUS    0.071352
MRUS  EXEL    0.071352
      AGEN    0.065702
AGEN  MRUS    0.065702
KOD   MRUS    0.057635
MRUS  KOD     0.057635
      CALA    0.038613
CALA  MRUS    0.038613
AXSM  MGNX    0.029837
MGNX  AXSM    0.029837
KOD   ABBV    0.024933
ABBV  KOD     0.024933
MGNX  MRUS    0.020743
MRUS  MGNX    0.020743
LGND  MRUS    0.015236
MRUS  LGND    0.015236
LLY   CALA    0.006927
CALA  LLY     0.006927
KOD   AXSM    0.002330
AXSM  KOD     0.002330
Length: 196, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	ABBV	AGEN	AXSM	CALA	CELG	EXEL	INCY	KOD	LGND	LLY	MGNX	MRUS	NVS	VRTX
2018-10-05	0.496934	0.260546	0.411413	0.912454	0.405407	0.250824	0.367630	0.440288	0.548427	0.743807	0.536610	0.280429	0.526441	0.469781
2018-10-08	0.485389	0.173144	0.184530	0.631872	0.552197	0.238152	0.389629	0.280529	0.377737	0.584142	0.548552	0.236645	0.540520	0.381482
2018-10-09	0.417127	0.139580	0.200659	0.705798	0.461232	0.332349	0.663593	0.346809	0.254886	0.539971	0.595148	0.181917	0.551555	0.506596
2018-10-10	0.289753	0.196344	0.270291	0.754223	0.286939	0.000000	0.301162	0.326862	0.000000	0.334588	0.492468	0.155004	0.470083	0.107777
2018-10-11	0.195230	0.404217	0.443488	1.000000	0.181198	0.229940	0.240395	0.425845	0.311657	0.000000	0.560285	0.392362	0.227780	0.459374

Normalized_Value.corr()

	ABBV	AGEN	AXSM	CALA	CELG	EXEL	INCY	KOD	LGND	LLY	MGNX	MRUS	NVS	VRTX
ABBV	1.000000	0.427637	0.074573	0.285213	0.598175	0.465816	0.356005	0.024933	0.554322	0.634248	0.376768	-0.127805	0.488510	0.654258
AGEN	0.427637	1.000000	0.087025	0.342340	0.345060	0.436806	0.337058	0.173264	0.428031	0.159665	0.364712	-0.065702	0.172740	0.398961
AXSM	0.074573	0.087025	1.000000	0.189099	0.199882	0.196105	0.135618	-0.002330	0.144460	0.137731	-0.029837	0.266485	0.210610	0.222292
CALA	0.285213	0.342340	0.189099	1.000000	0.254609	0.310993	0.265098	0.253076	0.234095	-0.006927	0.282364	-0.038613	0.106105	0.241100
CELG	0.598175	0.345060	0.199882	0.254609	1.000000	0.553212	0.592354	0.197497	0.577354	0.400445	0.553380	-0.128139	0.607962	0.735707
EXEL	0.465816	0.436806	0.196105	0.310993	0.553212	1.000000	0.552299	0.109840	0.479638	0.282657	0.334307	0.071352	0.378964	0.557054
INCY	0.356005	0.337058	0.135618	0.265098	0.592354	0.552299	1.000000	0.137852	0.457564	0.267662	0.319333	0.102754	0.588543	0.597989
KOD	0.024933	0.173264	-0.002330	0.253076	0.197497	0.109840	0.137852	1.000000	0.132109	-0.101754	0.443014	-0.057635	0.114513	0.111953
LGND	0.554322	0.428031	0.144460	0.234095	0.577354	0.479638	0.457564	0.132109	1.000000	0.334433	0.464213	0.015236	0.482527	0.664595
LLY	0.634248	0.159665	0.137731	-0.006927	0.400445	0.282657	0.267662	-0.101754	0.334433	1.000000	0.167876	0.073959	0.560906	0.489926
MGNX	0.376768	0.364712	-0.029837	0.282364	0.553380	0.334307	0.319333	0.443014	0.464213	0.167876	1.000000	0.020743	0.319734	0.339228
MRUS	-0.127805	-0.065702	0.266485	-0.038613	-0.128139	0.071352	0.102754	-0.057635	0.015236	0.073959	0.020743	1.000000	0.110201	-0.196105
NVS	0.488510	0.172740	0.210610	0.106105	0.607962	0.378964	0.588543	0.114513	0.482527	0.560906	0.319734	0.110201	1.000000	0.559522
VRTX	0.654258	0.398961	0.222292	0.241100	0.735707	0.557054	0.597989	0.111953	0.664595	0.489926	0.339228	-0.196105	0.559522	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

VRTX  VRTX    1.000000
NVS   NVS     1.000000
AGEN  AGEN    1.000000
AXSM  AXSM    1.000000
CALA  CALA    1.000000
CELG  CELG    1.000000
EXEL  EXEL    1.000000
INCY  INCY    1.000000
KOD   KOD     1.000000
LGND  LGND    1.000000
LLY   LLY     1.000000
MGNX  MGNX    1.000000
MRUS  MRUS    1.000000
ABBV  ABBV    1.000000
CELG  VRTX    0.735707
VRTX  CELG    0.735707
      LGND    0.664595
LGND  VRTX    0.664595
ABBV  VRTX    0.654258
VRTX  ABBV    0.654258
LLY   ABBV    0.634248
ABBV  LLY     0.634248
CELG  NVS     0.607962
NVS   CELG    0.607962
ABBV  CELG    0.598175
CELG  ABBV    0.598175
VRTX  INCY    0.597989
INCY  VRTX    0.597989
CELG  INCY    0.592354
INCY  CELG    0.592354
                ...   
MRUS  INCY    0.102754
INCY  MRUS    0.102754
LLY   KOD     0.101754
KOD   LLY     0.101754
AGEN  AXSM    0.087025
AXSM  AGEN    0.087025
ABBV  AXSM    0.074573
AXSM  ABBV    0.074573
LLY   MRUS    0.073959
MRUS  LLY     0.073959
EXEL  MRUS    0.071352
MRUS  EXEL    0.071352
      AGEN    0.065702
AGEN  MRUS    0.065702
KOD   MRUS    0.057635
MRUS  KOD     0.057635
      CALA    0.038613
CALA  MRUS    0.038613
AXSM  MGNX    0.029837
MGNX  AXSM    0.029837
KOD   ABBV    0.024933
ABBV  KOD     0.024933
MGNX  MRUS    0.020743
MRUS  MGNX    0.020743
LGND  MRUS    0.015236
MRUS  LGND    0.015236
LLY   CALA    0.006927
CALA  LLY     0.006927
KOD   AXSM    0.002330
AXSM  KOD     0.002330
Length: 196, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
ABBV    0.000118
AGEN    0.003431
AXSM   -0.001757
CALA   -0.003385
CELG   -0.005209
EXEL    0.002482
INCY   -0.000291
KOD    -0.003397
LGND   -0.009992
LLY     0.000657
MGNX   -0.007873
MRUS   -0.004364
NVS     0.000080
VRTX   -0.001864
dtype: float64
--------------------------------------------------
Stock risks:
ABBV    0.022021
AGEN    0.058816
AXSM    0.065432
CALA    0.036324
CELG    0.023185
EXEL    0.041567
INCY    0.028192
KOD     0.075263
LGND    0.034577
LLY     0.018396
MGNX    0.043203
MRUS    0.052393
NVS     0.013347
VRTX    0.025864
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
LGND	-0.009992	0.034577
MGNX	-0.007873	0.043203
CELG	-0.005209	0.023185
MRUS	-0.004364	0.052393
KOD	-0.003397	0.075263
CALA	-0.003385	0.036324
VRTX	-0.001864	0.025864
AXSM	-0.001757	0.065432
INCY	-0.000291	0.028192
NVS	0.000080	0.013347
ABBV	0.000118	0.022021
LLY	0.000657	0.018396
EXEL	0.002482	0.041567
AGEN	0.003431	0.058816

table.sort_values(by='Risk')

	Returns	Risk
NVS	0.000080	0.013347
LLY	0.000657	0.018396
ABBV	0.000118	0.022021
CELG	-0.005209	0.023185
VRTX	-0.001864	0.025864
INCY	-0.000291	0.028192
LGND	-0.009992	0.034577
CALA	-0.003385	0.036324
EXEL	0.002482	0.041567
MGNX	-0.007873	0.043203
MRUS	-0.004364	0.052393
AGEN	0.003431	0.058816
AXSM	-0.001757	0.065432
KOD	-0.003397	0.075263

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
ABBV	0.000118	0.022021	-0.448765
AGEN	0.003431	0.058816	-0.111694
AXSM	-0.001757	0.065432	-0.179680
CALA	-0.003385	0.036324	-0.368482
CELG	-0.005209	0.023185	-0.655988
EXEL	0.002482	0.041567	-0.180867
INCY	-0.000291	0.028192	-0.365033
KOD	-0.003397	0.075263	-0.178010
LGND	-0.009992	0.034577	-0.578186
LLY	0.000657	0.018396	-0.507891
MGNX	-0.007873	0.043203	-0.413701
MRUS	-0.004364	0.052393	-0.274157
NVS	0.000080	0.013347	-0.743256
VRTX	-0.001864	0.025864	-0.458699

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
ABBV	0.000118	0.022021	-0.448765	0.057985	-0.045839	0.002827	1.174272
AGEN	0.003431	0.058816	-0.111694	0.233831	-0.088710	-0.003802	-0.418410
AXSM	-0.001757	0.065432	-0.179680	0.293578	-0.147287	0.000000	29.357798
CALA	-0.003385	0.036324	-0.368482	0.075862	-0.095000	-0.008147	4.155844
CELG	-0.005209	0.023185	-0.655988	0.056006	-0.063432	-0.008344	2.658972
EXEL	0.002482	0.041567	-0.180867	0.164548	-0.071551	-0.003043	1.183122
INCY	-0.000291	0.028192	-0.365033	0.063346	-0.063346	-0.005289	2.103406
KOD	-0.003397	0.075263	-0.178010	0.257400	-0.155000	-0.015167	7.250755
LGND	-0.009992	0.034577	-0.578186	0.074011	-0.090059	-0.010718	-0.103062
LLY	0.000657	0.018396	-0.507891	0.040656	-0.047909	0.001650	1.330999
MGNX	-0.007873	0.043203	-0.413701	0.099398	-0.181597	-0.011316	0.713719
MRUS	-0.004364	0.052393	-0.274157	0.175549	-0.110269	-0.012640	1.966497
NVS	0.000080	0.013347	-0.743256	0.030908	-0.042722	-0.000224	0.645090
VRTX	-0.001864	0.025864	-0.458699	0.065368	-0.063616	0.000162	2.657669

table['Average Return Yearly'] = (1 + total_return)**(1 / number_of_years) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly
ABBV	0.000118	0.022021	-0.448765	0.057985	-0.045839	0.002827	1.174272	0.003899
AGEN	0.003431	0.058816	-0.111694	0.233831	-0.088710	-0.003802	-0.418410	-0.001397
AXSM	-0.001757	0.065432	-0.179680	0.293578	-0.147287	0.000000	29.357798	0.089593
CALA	-0.003385	0.036324	-0.368482	0.075862	-0.095000	-0.008147	4.155844	0.013665
CELG	-0.005209	0.023185	-0.655988	0.056006	-0.063432	-0.008344	2.658972	0.008786
EXEL	0.002482	0.041567	-0.180867	0.164548	-0.071551	-0.003043	1.183122	0.003928
INCY	-0.000291	0.028192	-0.365033	0.063346	-0.063346	-0.005289	2.103406	0.006963
KOD	-0.003397	0.075263	-0.178010	0.257400	-0.155000	-0.015167	7.250755	0.023607
LGND	-0.009992	0.034577	-0.578186	0.074011	-0.090059	-0.010718	-0.103062	-0.000344
LLY	0.000657	0.018396	-0.507891	0.040656	-0.047909	0.001650	1.330999	0.004417
MGNX	-0.007873	0.043203	-0.413701	0.099398	-0.181597	-0.011316	0.713719	0.002373
MRUS	-0.004364	0.052393	-0.274157	0.175549	-0.110269	-0.012640	1.966497	0.006512
NVS	0.000080	0.013347	-0.743256	0.030908	-0.042722	-0.000224	0.645090	0.002146
VRTX	-0.001864	0.025864	-0.458699	0.065368	-0.063616	0.000162	2.657669	0.008782

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly	CAGR
ABBV	0.000118	0.022021	-0.448765	0.057985	-0.045839	0.002827	1.174272	0.003899	0.143792
AGEN	0.003431	0.058816	-0.111694	0.233831	-0.088710	-0.003802	-0.418410	-0.001397	-0.134921
AXSM	-0.001757	0.065432	-0.179680	0.293578	-0.147287	0.000000	29.357798	0.089593	-0.286675
CALA	-0.003385	0.036324	-0.368482	0.075862	-0.095000	-0.008147	4.155844	0.013665	-0.132928
CELG	-0.005209	0.023185	-0.655988	0.056006	-0.063432	-0.008344	2.658972	0.008786	-0.130743
EXEL	0.002482	0.041567	-0.180867	0.164548	-0.071551	-0.003043	1.183122	0.003928	0.342455
INCY	-0.000291	0.028192	-0.365033	0.063346	-0.063346	-0.005289	2.103406	0.006963	-0.105964
KOD	-0.003397	0.075263	-0.178010	0.257400	-0.155000	-0.015167	7.250755	0.023607	NaN
LGND	-0.009992	0.034577	-0.578186	0.074011	-0.090059	-0.010718	-0.103062	-0.000344	0.066964
LLY	0.000657	0.018396	-0.507891	0.040656	-0.047909	0.001650	1.330999	0.004417	0.101140
MGNX	-0.007873	0.043203	-0.413701	0.099398	-0.181597	-0.011316	0.713719	0.002373	-0.176165
MRUS	-0.004364	0.052393	-0.274157	0.175549	-0.110269	-0.012640	1.966497	0.006512	NaN
NVS	0.000080	0.013347	-0.743256	0.030908	-0.042722	-0.000224	0.645090	0.002146	0.025861
VRTX	-0.001864	0.025864	-0.458699	0.065368	-0.063616	0.000162	2.657669	0.008782	0.071424

table.sort_values(by='Average Return Yearly')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Yearly	CAGR
AGEN	0.003431	0.058816	-0.111694	0.233831	-0.088710	-0.003802	-0.418410	-0.001397	-0.134921
LGND	-0.009992	0.034577	-0.578186	0.074011	-0.090059	-0.010718	-0.103062	-0.000344	0.066964
NVS	0.000080	0.013347	-0.743256	0.030908	-0.042722	-0.000224	0.645090	0.002146	0.025861
MGNX	-0.007873	0.043203	-0.413701	0.099398	-0.181597	-0.011316	0.713719	0.002373	-0.176165
ABBV	0.000118	0.022021	-0.448765	0.057985	-0.045839	0.002827	1.174272	0.003899	0.143792
EXEL	0.002482	0.041567	-0.180867	0.164548	-0.071551	-0.003043	1.183122	0.003928	0.342455
LLY	0.000657	0.018396	-0.507891	0.040656	-0.047909	0.001650	1.330999	0.004417	0.101140
MRUS	-0.004364	0.052393	-0.274157	0.175549	-0.110269	-0.012640	1.966497	0.006512	NaN
INCY	-0.000291	0.028192	-0.365033	0.063346	-0.063346	-0.005289	2.103406	0.006963	-0.105964
VRTX	-0.001864	0.025864	-0.458699	0.065368	-0.063616	0.000162	2.657669	0.008782	0.071424
CELG	-0.005209	0.023185	-0.655988	0.056006	-0.063432	-0.008344	2.658972	0.008786	-0.130743
CALA	-0.003385	0.036324	-0.368482	0.075862	-0.095000	-0.008147	4.155844	0.013665	-0.132928
KOD	-0.003397	0.075263	-0.178010	0.257400	-0.155000	-0.015167	7.250755	0.023607	NaN
AXSM	-0.001757	0.065432	-0.179680	0.293578	-0.147287	0.000000	29.357798	0.089593	-0.286675